Variance Reduction
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Variance Reduction
In mathematics, more specifically in the theory of Monte Carlo methods, variance reduction is a procedure used to increase the precision of the estimates obtained for a given simulation or computational effort. Every output random variable from the simulation is associated with a variance which limits the precision of the simulation results. In order to make a simulation statistically efficient, i.e., to obtain a greater precision and smaller confidence intervals for the output random variable of interest, variance reduction techniques can be used. The main ones are common random numbers, antithetic variates, control variates, importance sampling, stratified sampling, moment matching, conditional Monte Carlo and quasi random variables. For simulation with black-box models subset simulation and line sampling can also be used. Under these headings are a variety of specialized techniques; for example, particle transport simulations make extensive use of "weight windows" and ...
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Conditional Monte Carlo
Conditional (if then) may refer to: *Causal conditional, if X then Y, where X is a cause of Y *Conditional probability, the probability of an event A given that another event B has occurred *Conditional proof, in logic: a proof that asserts a conditional, and proves that the antecedent leads to the consequent *Strict conditional, in philosophy, logic, and mathematics *Material conditional, in propositional calculus, or logical calculus in mathematics * Relevance conditional, in relevance logic *Conditional (computer programming), a statement or expression in computer programming languages *A conditional expression in computer programming languages such as ?: *Conditions in a contract Grammar and linguistics *Conditional mood (or conditional tense), a verb form in many languages *Conditional sentence, a sentence type used to refer to hypothetical situations and their consequences **Indicative conditional, a conditional sentence expressing "if A then B" in a natural language **Cou ...
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Journal Of The Operations Research Society Of America
''Operations Research'' is a bimonthly peer-reviewed academic journal covering operations research that is published by the Institute for Operations Research and the Management Sciences. It was established in 1952 as the ''Journal of the Operations Research Society of America'' and obtained its current name in 1955. The editor-in-chief iJohn Birge(University of Chicago). Abstracting and indexing The journal is abstracted and indexed by '' Mathematical Reviews'', MathSciNet, Science Citation Index Expanded, Scopus, Social Sciences Citation Index, and ''Zentralblatt MATH''. According to the ''Journal Citation Reports'', the journal has a 2018 impact factor The impact factor (IF) or journal impact factor (JIF) of an academic journal is a scientometric index calculated by Clarivate that reflects the yearly mean number of citations of articles published in the last two years in a given journal, as ... of 2.604. References External links * Mathematics journals Publications ...
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Explained Variance
In statistics, explained variation measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. Often, variation is quantified as variance; then, the more specific term explained variance can be used. The complementary part of the total variation is called unexplained or residual variation. Definition in terms of information gain Information gain by better modelling Following Kent (1983), we use the Fraser information (Fraser 1965) :F(\theta) = \int \textrmr\,g(r)\,\ln f(r;\theta) where g(r) is the probability density of a random variable R\,, and f(r;\theta)\, with \theta\in\Theta_i (i=0,1\,) are two families of parametric models. Model family 0 is the simpler one, with a restricted parameter space \Theta_0\subset\Theta_1. Parameters are determined by maximum likelihood estimation, :\theta_i = \operatorname_ F(\theta). The information gain of model 1 over model 0 is written as :\Gamma(\theta_1:\theta_0) = 2 F(\theta_1 ...
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Central Limit Theorem
In probability theory, the central limit theorem (CLT) establishes that, in many situations, when independent random variables are summed up, their properly normalized sum tends toward a normal distribution even if the original variables themselves are not normally distributed. The theorem is a key concept in probability theory because it implies that probabilistic and statistical methods that work for normal distributions can be applicable to many problems involving other types of distributions. This theorem has seen many changes during the formal development of probability theory. Previous versions of the theorem date back to 1811, but in its modern general form, this fundamental result in probability theory was precisely stated as late as 1920, thereby serving as a bridge between classical and modern probability theory. If X_1, X_2, \dots, X_n, \dots are random samples drawn from a population with overall mean \mu and finite variance and if \bar_n is the sample mean of ...
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Independent And Identically Distributed Random Variables
In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent. This property is usually abbreviated as ''i.i.d.'', ''iid'', or ''IID''. IID was first defined in statistics and finds application in different fields such as data mining and signal processing. Introduction In statistics, we commonly deal with random samples. A random sample can be thought of as a set of objects that are chosen randomly. Or, more formally, it’s “a sequence of independent, identically distributed (IID) random variables”. In other words, the terms ''random sample'' and ''IID'' are basically one and the same. In statistics, we usually say “random sample,” but in probability it’s more common to say “IID.” * Identically Distributed means that there are no overall trends–the distribution doesn’t fluctuate and all items in th ...
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Probability Space
In probability theory, a probability space or a probability triple (\Omega, \mathcal, P) is a mathematical construct that provides a formal model of a random process or "experiment". For example, one can define a probability space which models the throwing of a die. A probability space consists of three elements:Stroock, D. W. (1999). Probability theory: an analytic view. Cambridge University Press. # A sample space, \Omega, which is the set of all possible outcomes. # An event space, which is a set of events \mathcal, an event being a set of outcomes in the sample space. # A probability function, which assigns each event in the event space a probability, which is a number between 0 and 1. In order to provide a sensible model of probability, these elements must satisfy a number of axioms, detailed in this article. In the example of the throw of a standard die, we would take the sample space to be \. For the event space, we could simply use the set of all subsets of the sampl ...
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Subset Simulation
Subset simulation is a method used in reliability engineering to compute small (i.e., rare event) failure probabilities encountered in engineering systems. The basic idea is to express a small failure probability as a product of larger conditional probabilities by introducing intermediate failure events. This conceptually converts the original rare event problem into a series of frequent event problems that are easier to solve. In the actual implementation, samples conditional on intermediate failure events are adaptively generated to gradually populate from the frequent to rare event region. These 'conditional samples' provide information for estimating the complementary cumulative distribution function (CCDF) of the quantity of interest (that governs failure), covering the high as well as the low probability regions. They can also be used for investigating the cause and consequence of failure events. The generation of conditional samples is not trivial but can be performed efficie ...
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Black-box
In science, computing, and engineering, a black box is a system which can be viewed in terms of its inputs and outputs (or transfer characteristics), without any knowledge of its internal workings. Its implementation is "opaque" (black). The term can be used to refer to many inner workings, such as those of a transistor, an engine, an algorithm, the human brain, or an institution or government. To analyse an open system with a typical "black box approach", only the behavior of the stimulus/response will be accounted for, to infer the (unknown) ''box''. The usual representation of this ''black box system'' is a data flow diagram centered in the box. The opposite of a black box is a system where the inner components or logic are available for inspection, which is most commonly referred to as a white box (sometimes also known as a "clear box" or a "glass box"). History The modern meaning of the term "black box" seems to have entered the English language around 1945. In electr ...
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Quasi Random Variables
Quasi (phonetics 'kwa - zee') is an American indie rock band formed in Portland, Oregon in 1993 by former spouses Sam Coomes ( vocals, guitar, rocksichord, various keyboards, bass) and Janet Weiss (vocals and drums). Joanna Bolme performed and recorded with the group as a bassist from 2007 to 2011. History In 1990, Sam Coomes, Janet Weiss, and Brad Pedinov formed the band Motorgoat. The band released two self-released cassettes and one 7" single before dissolving in 1993. Coomes and Weiss then began recording as a duo in 1993 under the name Quasi. They played with various additional musicians in early live appearances, but eventually settled on playing live as a duo as well. They self-recorded and self-released a cassette and a CD in 1993. They recorded ''R&B Transmogrification'' in the Portland band Pond's basement recording studio and released it on Up Records in 1997. They released two more albums with Up: ''Featuring "Birds"'' in 1998 and '' Field Studies'' in 1999; ...
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Moment Matching
Moment or Moments may refer to: * Present time Music * The Moments, American R&B vocal group Albums * ''Moment'' (Dark Tranquillity album), 2020 * ''Moment'' (Speed album), 1998 * ''Moments'' (Darude album) * ''Moments'' (Christine Guldbrandsen album) * ''Moments'' (Mark Holden album), 1995 * ''Moments'' (Leo Ku album) * ''Moments'' (Barbara Mandrell album) * ''Moments'' (Andrew Rayel album) * ''Moments'' (Boz Scaggs album) Songs * "Moment" (Blanche song) (2018) * "Moment" (SMAP song) (2012) * "Moment", a song by Aiden from ''Conviction'' * "Moment", a 2014 song by Young Money from '' Young Money: Rise of an Empire'', featuring Lil Wayne * "Moments" (Hans Bollandsås song) (2010) * "Moments" (Emerson Drive song) (2007) * "Moments" (Ayumi Hamasaki song) (2004) * "Moments" (Tove Lo song) (2014) * "Moments" (One Direction song) (2011) * "Moments" (Freddy Verano song) * "Moments", a song by Westlife from ''Westlife'' Film and television * ''Moment'' (film), a 1978 ...
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